Confirmation Bias Estimation from Electroencephalography with Machine Learning

Abstract

Cognitive biases are known to affect human decision making and can have disastrous effects in the fast-paced environments of military operators. Traditionally, post-hoc behavioral analysis is used to measure the level of bias in a decision. However, these techniques can be hindered by subjective factors and cannot be collected in real-time. This pilot study collects behavior patterns and physiological signals present during biased and unbiased decision-making. Supervised machine learning models are trained to find the relationship between Electroencephalography (EEG) signals and behavioral evidence of cognitive bias. Once trained, the models should infer the presence of confirmation bias during decision-making using only EEG - without the interruptions or the subjective nature of traditional confirmation bias estimation techniques.